Abstract:
The detection of malware presents a serious concern in cybersecurity and classic techniques such as signature-based detection and heuristic analysis are not generally effective, being unable to effectively meet the changing dynamic of current malware. The emerging forms of cyber threats continue to be more advanced that finding new and unknown types, not to mention obfuscated malware has become a daunting challenge. The conducted research deals with the implementation of the Convolutional Neural Networks (CNNs) with regard to the pattern-based detection of malware, which is the way to use deep learning techniques to transform malware binaries into visual representations that can then be classified according to the patterns they demonstrate. The research aims at converting malware binaries into grayscale images so that CNNs can automatically derive and discover complex features without any manual feature engineering. CNN models were tested and trained with popular datasets of malwares, such as Malimg, Drebin, and CICIDS 2017, and scored by following performance metrics such as precision, accuracy, recall, and F1-score. The performance was demonstrated by the fact that the CNN models had significantly better results as compared to the traditional machine learning classification models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests with the classification accuracy of more than 98 percent across a wide range of datasets. Moreover, the models were also tested in the terms of their resistance to violation by the adversarial attacks with the help of the adversarial training which helps to increase the resistance of the model to the manipulations that are aimed to mislead the classifier. Adversarial training has led to a vast improvement of the performance of the model on adversarial perturbed malware with the accuracy of the model on the examples amounting to 94.5 percent. The current study underscores the possible future of malware detection using CNN because it is considered a good alternative to conventional approaches that have failed to keep up with polymorphic, metamorphic, and obfuscated malware. The results further offer support to the idea that strong and dynamic solutions are crucial to the confrontation of outlining hostile challenges of cybersecurity. The paper is based on the big amount of data on deep learning regarding malware detection, and introduces a high-accuracy, scalable framework capable of real-world adoption in cybersecurity practices.
